
"""simple docstring"""


from collections.abc import Callable



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				float:







       """simple docstring"""
       UpperCamelCase       			=		a
       UpperCamelCase       			=		b
       if function(UpperCAmelCase_					) == 0:  # one of the a or b is a root for the function
              return a
       elif function(UpperCAmelCase_					) == 0:
              return b
       elif (
           function(UpperCAmelCase_					) * function(UpperCAmelCase_					) > 0
       ):  # if none of these are root and they are both positive or negative,
              # then this algorithm can't find the root
              raise ValueError("could not find root in given interval."					)
       else:
              UpperCamelCase       			=		start + (end - start) / 2.0
              while abs(start - mid					) > 10**-7:  # until precisely equals to 10^-7
                     if function(UpperCAmelCase_					) == 0:
                            return mid
                     elif function(UpperCAmelCase_					) * function(UpperCAmelCase_					) < 0:
                            UpperCamelCase       			=		mid
                     else:
                            UpperCamelCase       			=		mid
                     UpperCamelCase       			=		start + (end - start) / 2.0
              return mid



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				float:







       """simple docstring"""
       return x**3 - 2 * x - 5


if __name__ == "__main__":
      print(bisection(f, 1, 1_000))

      import doctest

      doctest.testmod()







"""simple docstring"""


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				float							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				bool = False							,   )->	str:







              """simple docstring"""
              super().__init__()

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		False

              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )

              UpperCamelCase       			=		TaConfig(
                  vocab_size=UpperCAmelCase_							,   d_model=UpperCAmelCase_							,   num_heads=UpperCAmelCase_							,   d_kv=UpperCAmelCase_							,   d_ff=UpperCAmelCase_							,   dropout_rate=UpperCAmelCase_							,   feed_forward_proj=UpperCAmelCase_							,   is_decoder=UpperCAmelCase_							,   is_encoder_decoder=UpperCAmelCase_							,   )

              UpperCamelCase       			=		nn.ModuleList()
              for lyr_num in range(UpperCAmelCase_      ):
                     UpperCamelCase       			=		TaBlock(UpperCAmelCase_      )
                     self.encoders.append(UpperCAmelCase_      )

              UpperCamelCase       			=		TaLayerNorm(UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.token_embedder(UpperCAmelCase_      )

              UpperCamelCase       			=		encoder_input_tokens.shape[1]
              UpperCamelCase       			=		torch.arange(UpperCAmelCase_							,   device=encoder_input_tokens.device      )
              x += self.position_encoding(UpperCAmelCase_      )

              UpperCamelCase       			=		self.dropout_pre(UpperCAmelCase_      )

              # inverted the attention mask
              UpperCamelCase       			=		encoder_input_tokens.size()
              UpperCamelCase       			=		self.get_extended_attention_mask(UpperCAmelCase_							,   UpperCAmelCase_      )

              for lyr in self.encoders:
                     UpperCamelCase       			=		lyr(UpperCAmelCase_							,   UpperCAmelCase_      )[0]
              UpperCamelCase       			=		self.layer_norm(UpperCAmelCase_      )

              return self.dropout_post(UpperCAmelCase_      ), encoder_inputs_mask





